Power system scheduling method based on wind and light output scene and virtual energy storage model

Abstract

The invention relates to a power system scheduling method based on a wind and light output scene and a virtual energy storage model, and the method comprises the steps: sampling and generating day-ahead wind and light data, and carrying out the scene reduction to obtain a typical wind and light output scene; constructing a virtual energy storage model based on the electric vehicle, and introducing the deviation electric quantity to correct the virtual energy storage model; constructing a day-ahead stage multi-agent collaborative optimization model taking the minimum operation cost of an energy storage operator and the minimum energy consumption cost of the park integrated energy system as optimization objectives; and solving the day-ahead stage multi-subject collaborative optimization model by adopting an improved sparrow search optimization algorithm to obtain an optimal solution, namely an optimal scheduling scheme. A typical wind and light output scene set is constructed, the influence of long-time scale prediction deviation on a scheduling plan is reduced, and the robustness of a power system is enhanced; an electric vehicle cluster is modeled as virtual energy storage, the flexible adjustment potential of the electric vehicle cluster is mined through load-storage dual characteristics, a space-time complementary mechanism is formed with physical energy storage, and the overall energy efficiency of the system is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of optimal operation of integrated energy systems, and particularly relates to a power system dispatching method based on wind and light output scenarios and a virtual energy storage model. BACKGROUND

[0002] As an energy-using device with "charge-storage" dual properties, the large-scale cluster of electric vehicles (EVs) can act as a virtual energy storage (VES) resource and form a multi-energy complementary and collaborative mechanism with other energy storage resources in an integrated energy system to optimize energy distribution in the time and space dimensions. However, in the traditional system, the grid-connection and off-grid time of individual EVs is affected by the time and space coupling uncertainty of user travel habits, which limits the adjustable margin of the EV cluster and increases the regulation cost. In contrast, the park integrated energy system (PIES) relies on structured energy use scenarios to significantly improve the time and space analyzability of EV user charging behavior and the willingness to participate in response.

[0003] As a flexible energy storage resource, the electric vehicle can effectively reduce the energy storage cost and the load fluctuation of the system. At present, scholars at home and abroad have carried out research on the optimization and dispatching problem of VES mainly composed of electric vehicles.

[0004] The paper "Multi-element peak shaving auxiliary service optimization supported by electric vehicle virtual energy storage" by Hou Hui et al. published in the S1 issue of 2024 "Chinese Journal of Electrical Engineering" discloses a compensation mechanism considering the peak shaving contribution of electric vehicles, and involves electric vehicles as flexible resources in peak shaving to effectively reduce the system peak-valley difference and peak shaving cost. The paper "Multi-microgrid hybrid game operation strategy considering virtual energy storage participation" by Wu Ruixing et al. published in the 4th issue of 2025 "Power Grid Technology" discloses an integrated energy system optimization and dispatching model containing electric vehicles, adopts a dual optimization method considering the energy storage charging and discharging strategy and system cost, and improves the economic efficiency and stability of the system. The paper "Multi-microgrid integrated energy system optimization and dispatching considering EV coordinated charging and reward-punishment step carbon trading" by Gao Ya et al. published in the 1st issue of 2025 "Power Construction" adds a virtual energy storage model in the hybrid game of multi-microgrid, which improves the renewable energy consumption in the microgrid system and the income of the microgrid operator. The above-mentioned documents optimize and dispatch the integrated energy system containing VES, but ignore the benefit demands of VES as an independent subject.

[0005] The above-mentioned research has some research on the two-stage optimization and dispatching containing VES, but still has the following shortcomings. On the one hand, only the optimization of a single subject is considered, and the benefits of each subject when multiple subjects participate in two-stage optimization and dispatching cannot be considered; on the other hand, for the optimization and dispatching problem of multiple subjects, the influence of virtual energy storage participation on the optimization result is not considered. Summary of the Invention

[0006] The purpose of this invention is to address the above-mentioned problems by providing a power system dispatching method based on wind and solar power output scenarios and a virtual energy storage model. This method constructs a virtual energy storage model with electric vehicles as the main component and considers the impact of carbon prices on dispatching results. It optimizes the dispatching of a multi-entity collaborative optimization model that includes the integrated energy system of the park and energy storage operators in the day-ahead phase.

[0007] To achieve the above objectives, the technical solution provided by this invention is as follows: The power system dispatching method based on wind and solar power output scenarios and virtual energy storage models includes the following steps: In step 1, the impact of uncertainties in wind and solar power output is considered, and the wind and solar power output under typical scenarios is analyzed. Assume that wind power and solar power output follow normal distributions. , , , These are the expected output values ​​for wind power and solar power, respectively. , These are the standard deviations of wind power and solar power output, respectively. The Latin hypercube sampling method is used to generate a large number of wind and solar power output scenarios that satisfy the probability distribution, and the generated scenarios are then reduced.

[0008] Preferably, in step 2, the grid connection and off-grid times of electric vehicles are relatively uniform, and their aggregation is regarded as an electric vehicle cluster for comprehensive regulation. The dispatchable power boundary and battery capacity boundary of the electric vehicle cluster are: (1) (2) In the formula: , , , These represent the maximum charging and discharging power and the maximum and minimum battery capacity of the electric vehicle cluster at time t, respectively; N is the total number of electric vehicles. These are the state parameters of the electric vehicle. The values ​​1 and 0 respectively describe the grid-connected and grid-off states of electric vehicles; , , , These are the charging power, discharging power, and maximum and minimum battery capacity of the k-th electric vehicle, respectively. To adjust the step size.

[0009] Furthermore, in step 2, the step power generation caused by multiple electric vehicles simultaneously connecting to or disconnecting from the grid is addressed. A deviation in electrical charge is introduced to correct it. (3) In the formula: , The energy level of the kth electric vehicle when it is connected to and disconnected from the grid; Let t-1 represent the grid connection / disconnection status of the kth electric vehicle.

[0010] Preferably, in step 2, the virtual energy storage model of the electric vehicle cluster is as follows: (4) In the formula: , , , These are the energy storage capacity of the electric vehicle cluster, the power exchange with the power grid, the charging power, and the discharging power.

[0011] Furthermore, in step 3, the objective function of the multi-agent collaborative optimization model includes minimizing the operating cost of the energy storage operator. (5) (6) In the formula: For the operating costs of energy storage operators; , These are the costs of physical energy storage and the costs of virtual energy storage, respectively. , , , The system electricity price, electric vehicle charging service fee, energy storage electricity sales price, and the electricity sales price of the park's integrated energy system; This refers to the energy storage charging and discharging cost coefficient. Cost of discharging electric vehicles; , This refers to the power volume that energy storage operators purchase from distribution network operators and the power volume they sell to the park's integrated energy system. This refers to the electricity sold by the park's integrated energy system to energy storage operators.

[0012] Furthermore, the objective function of the multi-agent collaborative optimization model also includes minimizing the energy cost of the park's integrated energy system. (7) (8) In the formula: , , , These are the costs of purchasing gas, carbon emissions, purchasing electricity, and operation and maintenance for the park's integrated energy system. Electricity is purchased from the power distribution network operator for the park's integrated energy system; , The system uses time-of-use gas pricing and gas purchase volume; , , For energy storage The charging and discharging power and the charging and discharging cost coefficient; , The operation and maintenance cost coefficient and output power of energy conversion equipment x, including combined cooling, heating and power units, power-to-gas and carbon capture coupling devices, gas boilers, electric boilers, and electric chillers; The power balance constraint condition of the multi-agent collaborative optimization model is as follows: (9) In the formula: , , The electricity, heat, and cooling loads of the park's integrated energy system; , Contribute to the system's scenery; , , These refer to the power output of the combined cooling, heating and power (CCHP) unit, specifically the power generated by electricity, heat, and cooling. Gas purchase volume for combined cooling, heating and power units; Carbon capture devices consume energy to capture carbon. , , , These are the power consumption and hydrogen output of the electric chiller, and the power consumption and gas output of the methane reactor, respectively. , This refers to the heat output power and gas output power of the gas-fired boiler. , The heat output and electrical power of the electric boiler; , The cooling power and electrical power of the ERU; , yes The charging and discharging power of the cold energy storage device at all times; , These refer to the charging and discharging power of the thermal energy storage equipment.

[0013] Preferably, in step 3, the impact of carbon price on the optimization results is considered, and a carbon price model is constructed: (10) In the formula: , For carbon emission costs and actual net emissions; , Based on the carbon price and the carbon price growth rate; is the length of the carbon growth interval;

[0014] ; (11) wherein: , is the actual carbon emission and carbon emission quota; , is the actual carbon emission coefficient and carbon emission quota per unit of electricity; , , , is the actual carbon emission coefficient and carbon emission quota per unit of gas for gas boilers and combined heat and power units; is the carbon absorption coefficient of the methane reactor when producing gas; , is the conversion coefficient of electric heat conversion and cold heat conversion.

[0015] Preferably, in step 4, the improved sparrow search optimization algorithm is used to solve the day-ahead stage multi-agent collaborative optimization model to obtain the optimal scheduling scheme of the power distribution network system.

[0016] The improved sparrow search optimization algorithm adopts an improved sparrow species dynamic adjustment strategy: The explorers in the sparrow population mainly perform global search, while the activities of the followers are divided into two categories; part of the followers perform local search at the best position of the explorers, while the other part performs global search; the number of explorers and followers in the sparrow population is fixed in the original sparrow search algorithm, and a fixed number of explorers always perform global search in each iteration; then, the followers search according to the direction provided by the explorers; once the ideal position of the followers falls into the local optimum, a certain number of followers will perform local search at the best point, and then perform global search, so it is difficult to exit the local optimum; In the later stage of the iteration process, more followers are needed to perform global search in order to explore better sites in various places; in addition, a larger proportion of sparrows need to perform global search in the followers; in order to balance the global search and local search capabilities, a dynamic adjustment strategy for the number of sparrow explorers and followers is adopted,

[0017] wherein: is the proportion of improved explorers; is the proportion of original explorers; is the upper limit of the increase in the set explorer ratio column; is the follower of the sparrow in the population; is the originally set proportion; is the upper limit of the ratio of searchers sparrow; with the increase of the number of iterations, the number of explorers and followers increases, and the number of followers searching near the optimal position of the explorer decreases, which helps the algorithm to jump out of the local optimum and increase the global search ability.

[0018] Compared with the prior art, the beneficial effects of the present application include: 1) Improve the ability of the system to cope with uncertainty, construct a typical wind power output scene set by Latin hypercube sampling and scene reduction technology, effectively represent the random volatility of renewable energy, reduce the influence of long time scale prediction deviation on dispatching plan, and enhance the robustness of the system. Combined with the two-stage optimization architecture, the connection between day-ahead planning and intra-day dynamic adjustment is realized, and the operation risk caused by source and load prediction error is relieved.

[0019] 2) Realize efficient collaborative configuration of multi-energy resources, innovatively model large-scale electric vehicle clusters as virtual energy storage (VES), and explore their flexible adjustment potential through "load-storage" dual characteristics, and form a time and space complementary mechanism with physical energy storage. Integrate the electricity-carbon price signal in the day-ahead optimization, guide the multi-agent collaborative interaction of VES and park integrated energy system (PIES), energy storage operators, optimize the charging and discharging strategy of multiple types of energy storage resources, and improve the overall energy efficiency of the system.

[0020] 3) Balance the low-carbon target and the interests of multiple agents, introduce a carbon price model to quantify the system's carbon emission cost, drive multiple agents to actively participate in low-carbon dispatching through carbon quota constraints and income incentives. Based on the master-slave game framework, build a collaborative optimization model led by distribution network operators (DNO), use dynamic pricing strategies to coordinate the interests of leaders and followers (energy storage operators, PIES), reasonably distribute the benefits of all parties while reducing the system's energy cost, and use the improved sparrow optimization algorithm to search for the global optimal solution, improve the feasibility of the optimal solution.

[0021] 4) Strengthen the model's empirical nature and promotional value, verify the model's comprehensive benefits in reducing operating costs (economy), improving new energy consumption rate (efficiency), and reducing carbon emissions (low carbon), etc. The method has a clear engineering landing path, and can provide a standardized technical framework for multi-agent collaborative park-level energy system optimization, and help the construction of new power system under the "double carbon" target. BRIEF DESCRIPTION OF DRAWINGS

[0022] The present application will be further described below in conjunction with the drawings and examples.

[0023] Figure 1 is a structural schematic diagram of a comprehensive energy system according to an embodiment of the present application.

[0024] Figure 2 is a wind power output prediction curve diagram according to an embodiment of the present application.

[0025] Figure 3 is a photovoltaic output prediction curve diagram of an embodiment of the present application.

[0026] Figure 4 is a typical load curve diagram of an embodiment of the present application.

[0027] Figure 5 is a day-ahead electric load optimization result provided by an embodiment of the present application.

[0028] Figure 6 is a day-ahead thermal load optimization result provided by an embodiment of the present application.

[0029] Figure 7 is a day-ahead cold load optimization result provided by an embodiment of the present application. DETAILED DESCRIPTION

[0030] The park comprehensive energy system of the embodiment is as shown in Figure 1 .

[0031] The power system scheduling method based on wind and light output scenarios and a virtual energy storage model comprises: Step 1: Latin hypercube sampling is used to generate day-ahead wind and light data, and typical scenarios are obtained through scenario reduction; In the embodiment, when the influence of wind and light output uncertainty on the result is considered, the wind and light output under the typical scenario should be analyzed. It is assumed that the wind and light output respectively obeys normal distribution 、 , 、 are the expected value of wind power output and the expected value of photovoltaic output, 、 are the percentage of wind power output and the percentage of photovoltaic output, respectively, and Latin hypercube sampling method is used for scenario generation, generating 1000 wind and light output scenarios satisfying the probability distribution.

[0032] The generated scenarios are reduced, and the 999 scenarios with the lowest probability are reduced to obtain the typical scenario with the largest probability that may occur.

[0033] Figure 3 The typical load curve of the embodiment is as shown in the figure, including the predicted wind power output, photovoltaic output, electric load, cold load and thermal load curve.

[0034] Step 2: A virtual energy storage model mainly based on electric vehicles is constructed, and a deviation electric quantity is introduced to correct the virtual energy storage model; Since the grid-connected and off-grid time of EVs in the park is relatively unified, the EVs can be aggregated and considered as an EV cluster for comprehensive regulation and control. The adjustable power boundary and battery capacity boundary of the EV cluster are: ; (1) ; (2) In the formula: , , , respectively represent the maximum charging and discharging power and the maximum and minimum battery capacity of the electric vehicle cluster at time t; N is the total number of electric vehicles; is the state parameter of the electric vehicle, take 1 and 0 to describe the grid-connected state and off-grid state of the electric vehicle respectively; , , , are the charging power, discharging power and maximum and minimum battery capacity of the kth electric vehicle respectively; is the adjustment step.

[0035] In order to solve the step change in power when multiple electric vehicles are simultaneously connected to the grid or off-grid , a deviation power is introduced to correct it, ; (3) In the formula: , is the power of the kth electric vehicle when it is connected to the grid or off-grid; is the grid-connected and off-grid state of the kth electric vehicle at time t-1.

[0036] Then the virtual energy storage model of the EV cluster is: ; (4) In the formula: , , , are the energy storage capacity, interaction power with the grid, charging power and discharging power of the EV cluster respectively.

[0037] Step 3: Consider the influence of carbon price on the optimization result in the optimization process, establish a carbon price model, and construct a day-ahead stage multi-agent collaborative optimization model with the minimum operating cost of the energy storage operator and the minimum energy consumption cost of the park comprehensive energy system as the optimization objectives.

[0038] In order to tap the collaborative optimization potential of DNO, PIES and energy storage operators, a day-ahead stage model is constructed, in which the upper layer takes the maximum DNO electricity sales revenue as the target, and guides the energy consumption behavior of the lower layer through electricity price mechanism design; the lower layer joint optimization model coordinates the energy storage operator's revenue and PIES's comprehensive energy consumption cost. Through centralized and distributed solution of the model, the benefit equilibrium solution that guarantees the revenue of each party is obtained. The day-ahead dispatching task execution cycle is 24h, and the time step is 1h.

[0039] Objective 1: the minimum cost of energy storage operator, ; (5) ; (6) In the formula: is the cost of energy storage operator; , are the physical energy storage cost and virtual energy storage cost, respectively; , , , are the system electricity price, electric vehicle charging service fee, energy storage electricity selling price and PIES electricity selling price; is the energy storage charging and discharging cost coefficient; is the electric vehicle discharging subsidy; , are the energy storage operator's electricity purchase power from DNO and electricity selling power to PIES; is the electricity selling power of PIES to energy storage operator.

[0040] Objective 2: the minimum cost of PIES energy use, ; (7) ; (8) In the formula: , , , are the purchase gas, carbon emission, electricity purchase and operation and maintenance costs of PIES, respectively; is the electricity purchase amount of PIES from DNO; , are the system time-of-use gas price and gas purchase amount; , , are the charging and discharging power and charging and discharging cost coefficient of energy storage ; , are the operation and maintenance cost coefficient and output power of energy conversion equipment x, including CCS, P2G, CCHP, GB, EB and ERU.

[0041] The power balance constraint condition of the model is: ; (9) In the formula: , , are the electric heating and cooling loads of PIES; , are the wind and solar output in the system; , , are the power of electricity, heat and cold produced by the CCHP unit, respectively; is the gas consumption of the CCHP unit; is the energy consumption of CCS; , , , are the power consumption and hydrogen production power of EL and the power consumption and gas production power of MR, respectively; , are the heat production power and gas power of GB; , are the heat production power and electricity power of EB; , are the cold production power and electricity power of ERU; , is the power of charging and discharging of the cold energy storage device; , are the power of charging and discharging of the heat energy storage device. The carbon price model is:

[0042] ; (10) In the formula: , is the carbon emission cost and the actual net emission amount; , is the basic carbon price and the carbon price growth rate; is the length of the carbon amount growth interval.

[0043]

[0044] ; (11) In the formula: , is the actual carbon emission amount and the carbon emission quota; , is the actual carbon emission coefficient and the carbon emission quota per unit of electricity; , , , is the actual carbon emission coefficient and the carbon emission quota per unit of gas of GB and CCHP; is the carbon absorption coefficient of MR when producing gas; , is the conversion coefficient of electricity to heat and cold to heat.

[0045] Step 4: The improved sparrow search optimization algorithm is used to solve the multi-agent collaborative optimization model in the day-ahead stage, and the optimal solution, i.e., the optimal scheduling scheme, is obtained.

[0046] ​In nature, sparrows are divided into explorers, followers and scouts. Explorers are sparrows that can find better food; followers are influenced by the direction of explorers and follow them to find the best food; scouts are always alert to the dangers around them and will make corresponding escape behavior when they find foragers.

[0047] The population consisting of n sparrows can be expressed as: ; (12) In the formula, d is the dimension of the problem to be solved; n is the total number of sparrows.

[0048] The searcher position update formula is: ; (13) In the formula: is the position of the ith explorer, ; r is the number of iterations; R is the maximum number of iterations; is a random number between 0 and 1; Q is a random number following a normal distribution; L is a 1xd matrix, each element of which is 1; Z, respectively represent the warning value and the safety value; The follower position update formula is: ; (14) In the formula: is the current optimal position; is the global worst position; A is a 1xd matrix, the elements of which are randomly 1 or -1, and ; The scout position update formula is: ; (15) In the formula: is the current global optimal position; is the step control coefficient, which is a random number following a normal distribution with a mean of 0 and a variance of 1; m is a random number between 0 and 1; is the fitness of sparrow i; , represent the current global worst and best fitness; is a constant to avoid the denominator from being 0.

[0049] The explorers in the sparrow population mainly perform global search, and the activities of the followers are divided into two categories. Part of the followers perform local search at the optimal position of the explorer, and the other part performs global search. The number of explorers and followers in the sparrow population is fixed in the original sparrow search algorithm, and a fixed number of explorers always perform global search in each iteration. Then, the connector searches according to the direction provided by the explorer. Once the ideal position of the follower falls into the local optimum, a certain number of followers will perform local search at the best point, and then perform global search, so it is difficult to exit the local optimum. In the later stage of the iteration process of the algorithm, more followers are needed to perform global search in order to explore better sites all over the world. In addition, a larger proportion of sparrows need to perform global search in the followers.

[0050] In order to balance the ability of the algorithm to perform global and local search, a dynamic adjustment strategy for the number of sparrow explorers and followers is adopted,

[0051] In the formula: is the proportion of improved explorers; is the proportion of original explorers, generally set to 20%; is the upper limit of the increase of the set explorer ratio column, equal to 0.1; is the follower of the sparrow in the population, used for global search; is the original set proportion, generally set to 0.5; is the upper limit of the increased searcher sparrow proportion, taken as 0.1. With the increase of the number of iterations, the number of explorers and followers increases, and the number of followers searching near the optimal position of the explorer decreases, which is beneficial to the algorithm to jump out of the local optimum and increase the global search ability.

[0052] Step 5: According to the optimal scheduling scheme obtained in step 4, guide the optimal operation of the power distribution network system.

[0053] Example: The present application studies the multi-agent collaborative optimization strategy in the power distribution network system, constructs a day-ahead collaborative optimization model including three agents of power distribution network operator, energy storage operator and park integrated energy system, and solves the model by calling Cplex under the version of MATLA-R2024a. In order to verify the economy and low carbon of the model in system optimization, the present application takes a park integrated energy system in Sichuan as an example for simulation analysis. Latin hypercube is used to generate 1000 sets of wind and light output data, as shown in Figures 2-3 . The generated data is reduced to obtain typical wind and light output, as shown in Figure 4As shown in Table 1, the VES parameters, primarily for electric vehicles, are used to generate the charging demand for electric vehicles within the park using the Monte Carlo method.

[0054] Table 1 Virtual Energy Storage Parameters

[0055] Power balance analysis, by Figure 5 The power balance diagram shown reflects the dynamic matching of power supply and demand in the park, including the output of distributed energy sources such as wind power, photovoltaics, and CCHP, as well as the regulating role of energy storage and external power purchases. Figure 5 It is evident that renewable energy makes a significant contribution to the integrated energy system of the park. Wind and solar power output is high during the daytime hours of 8:00-16:00, especially with solar power approaching its peak at midday, reaching a maximum output of approximately 4000 kW. This overlaps with the peak industrial load, reducing the overall energy system's electricity purchase demand. Meanwhile, the CCHP (Content Capacity Utilization Power Supply) has flexible power supply capabilities, with high output during the nighttime hours of 20:00-24:00 and the morning peak hours of 04:00-08:00, compensating for power shortages during periods of insufficient wind and solar power. Furthermore, energy storage plays a crucial role. Virtual and physical energy storage charges during the off-peak hours of 00:00-04:00 and discharges during the peak hours of 12:00-16:00, smoothing the load curve and reducing the amount of electricity purchased by the distribution network operator. However, during the evening peak hours of 18:00-20:00, solar power output drops sharply, and the combined power supply from CCHP and energy storage still faces a shortage of approximately 1000 kW, requiring reliance on electricity purchases from the distribution network operator. This period has significant optimization potential. Therefore, it is advisable to increase energy storage capacity and the amount of energy stored during off-peak hours to cover longer periods of high load.

[0056] Thermal power balance analysis, by Figure 6 The heat power balance diagram shown illustrates the coordinated operation of the CCHP (Gas-fired Power Boiler), GB (Gas-fired Boiler), EB (Electric Boiler), and thermal storage. The diagram reveals that the CCHP dominates the heat supply, with stable heat output throughout the day, particularly covering most of the base heat load during the nighttime period (00:00-08:00). Simultaneously, thermal storage plays a significant role in peak shaving and valley filling. Thermal storage absorbs excess heat during the daytime period (08:00-12:00), with peak heat input reaching 3000 kW, and releases it during the evening peak (118:00-20:00), optimizing the system's energy purchase strategy and improving its economic efficiency. Furthermore, GB and EB have significant complementary effects. GB supplements heat during the 20:00-24:00 period, while EB provides flexibility through electricity conversion, but its high power consumption may lead to power shortages. The current-day heat load curve largely matches the heat supply, with only slight redundancy during the 16:00-18:00 period. Therefore, it is necessary to optimize the thermal storage release strategy to improve energy utilization.

[0057] Cold power balance analysis, by Figure 7 The cold power balance chart shown in the figure embodies the cooperative operation effect of CCHP, ERU (waste heat refrigeration unit) and cold energy storage. The efficient cooperation of CCHP and ERU meets the cooling demand in the system, and the cooling capacity of CCHP is superimposed with the cooling capacity of ERU during the day 08:00-16:00, covering the cooling load peak and meeting the cooling demand in the system. At the same time, the regulation of cold energy storage provides flexible energy charging and discharging strategy for the system. The cold energy storage stores cold about 1000 kW at night 000:00-06:00, and releases it in the afternoon 12:00-14:00, optimizing the energy use scheme of the system and improving the economy of the system. At present, the cooling load curve and the total cooling capacity are generally matched, but there is a gap of about 500 kW in the evening 18:00-20:00, which needs to rely on the energy storage for rapid response.

[0058] Table 2 The benefits of each subject with or without virtual energy storage

[0059] Economic analysis, as can be seen from Table 2, the addition of virtual energy storage in scheme 2 enables the energy storage operator to obtain income through peak-valley arbitrage strategy, and to increase income by 4856.1 yuan through charging service fee of EV; on the other hand, the addition of virtual energy storage of electric vehicles increases the power consumption of the system, which promotes the DNO to increase the sales of electricity by 4361.98 yuan; in addition, as a VES resource, EV improves the charging and discharging power of the energy storage operator, and replaces the PIES gas consumption with low-price electricity, which reduces the carbon emission of the park while reducing the energy cost. In summary, compared with scheme 1, the addition of virtual energy storage in scheme 2 not only improves the interests of the three parties, but also reduces the total carbon emission of the system.

[0060] The present application is aimed at the multi-subject collaborative optimization problem involving virtual energy storage, improves the ability of the system to cope with uncertainty, constructs a typical wind and light output scene set through Latin hypercube sampling and scene reduction technology. The electric vehicle cluster is modeled as virtual energy storage (VES), and its flexible regulation potential is excavated through the "load-storage" dual characteristics, a day-ahead multi-subject collaborative optimization model considering the participation of virtual energy storage is proposed, and the following conclusions are obtained through theoretical analysis and simulation verification: (1) The wind and light output data is generated by Latin hypercube sampling, and the typical uncertainty scene is extracted by combining the scene reduction technology; at the same time, a virtual energy storage model is constructed with the electric vehicle cluster as the core, which represents its charging and discharging flexibility and energy aggregation characteristics.

[0061] (2) In the case of considering carbon price, a double-subject collaborative optimization framework of park integrated energy system and energy storage operator is established, which integrates economic target, low-carbon target, carbon price model and electric-thermal-cold multi-energy flow balance constraint, realizes multi-energy complementation and benefit cooperation.

[0062] (3) Taking a certain park in Sichuan as the empirical object, the impact of virtual energy storage participation and carbon price on system operating costs and carbon emission intensity is analyzed by simulation and quantitative analysis to verify the comprehensive benefits of the proposed method in improving economic efficiency and low carbon emissions.

Claims

1. A power system dispatching method based on wind and solar power output scenarios and virtual energy storage models, characterized in that, The method comprises the following steps: Step 1: generating day-ahead wind and light data by sampling with Latin hypercube sampling method, and performing scene reduction to obtain typical wind and light output scenes; Step 2: constructing a virtual energy storage model taking electric vehicles as the main part, and introducing a deviation power to correct the virtual energy storage model; Step 3: constructing a day-ahead multi-agent collaborative optimization model with the minimum operation cost of an energy storage operator and the minimum energy consumption cost of a park comprehensive energy system as the optimization objectives; Step 4: solving the day-ahead multi-agent collaborative optimization model to obtain an optimal solution, i.e., an optimal scheduling scheme; Step 5: guiding the optimal operation of a power distribution network system according to the optimal scheduling scheme obtained in Step 4.

2. The power system dispatching method of claim 1, wherein, In Step 1, the wind and light outputs in typical scenes are analyzed by considering the influence of wind and light output uncertainty; Assume that the wind power and photovoltaic power outputs follow normal distribution , , , are the expected values of the wind power and photovoltaic power outputs, respectively, , are the standard deviations of the wind power and photovoltaic power outputs, respectively. Latin hypercube sampling is used to generate scenarios, generate a large number of wind and light output scenarios that meet the probability distribution, and cut the generated scenarios.

3. The power system dispatching method of claim 2, wherein, In Step 2, the grid-connected and off-grid times of electric vehicles are unified, and the electric vehicles are aggregated to be regarded as an electric vehicle cluster for comprehensive regulation and control, and the adjustable power boundary and the battery power boundary of the electric vehicle cluster are: ；（1） ；（2） In the formula: , , , respectively represent the maximum charging and discharging power and the maximum and minimum battery capacity of the electric vehicle cluster at time t; N is the total number of electric vehicles; is a state parameter of the electric vehicle, takes 1 and 0 to respectively describe the grid-connected state and off-grid state of the electric vehicle; , , , are the charging power, discharging power and maximum and minimum battery capacity of the kth electric vehicle, respectively; is the adjustment step.

4. The power system dispatching method of claim 3, wherein, In step 2, the step power generated by the simultaneous grid-connection or off-grid of multiple electric vehicles , the deviation power is introduced to correct it, ；（3） In the formula: , The energy level of the kth electric vehicle when it is connected to and disconnected from the grid; Let t-1 represent the grid connection / disconnection status of the kth electric vehicle.

5. The power system dispatching method of claim 4, wherein, In Step 2, the virtual energy storage model of the electric vehicle cluster is: ；（4） In the formula: , , , are the energy storage capacity of the electric vehicle cluster, the interaction power with the power grid, the charging power, and the discharging power, respectively.

6. The power system dispatching method of claim 5, wherein, In Step 3, the objective function of the multi-agent collaborative optimization model comprises the minimum operation cost of the energy storage operator, ；（5） ； （6） In the formula: is the operating cost of the energy storage operator; , are the physical energy storage cost and the virtual energy storage cost, respectively; , , , is the system electricity price, the electric vehicle charging service fee, the energy storage electricity selling price, and the park integrated energy system electricity selling price; is the energy storage charging and discharging cost coefficient; is the electric vehicle discharging cost; , is the electricity purchasing power of the energy storage operator from the power distribution network operation unit and the electricity selling power to the park integrated energy system; is the electricity selling power of the park integrated energy system to the energy storage operator.

7. The power system dispatching method of claim 6, wherein, The objective function of the multi-agent collaborative optimization model also comprises the minimum energy consumption cost of the park comprehensive energy system, ；（7） ；（8） In the formula: , , , respectively are the cost of gas purchase, carbon emission, electricity purchase and operation and maintenance of the park comprehensive energy system; is the electricity purchase quantity of the park comprehensive energy system from the power distribution network operation unit; , is the time-sharing gas price and gas purchase quantity of the system; , , is the charge and discharge power and charge and discharge cost coefficient of the energy storage ; , is the operation and maintenance cost coefficient and output power of the energy conversion equipment x, including combined heat and power units, electric-gas coupling devices and carbon capture, gas boilers, electric boilers, electric chillers; The power balance constraint condition of the multi-agent collaborative optimization model is: ；（9） In the formula: , , is the electricity, heat and cold load of the park comprehensive energy system; , is the wind and light output in the system; , , is the power of electricity, heat and cold produced by the combined heat and power unit respectively; is the gas purchase quantity of the combined heat and power unit; is the carbon capture energy consumption of the carbon capture device; , , , is the power consumption and hydrogen output power of the electric refrigerator respectively, and the power consumption and gas output power of the methane reactor; , is the heat output power and gas power of the gas boiler; , is the heat output power and electric power of the electric boiler; , is the cold power and electric power of the ERU; , is the charging and discharging power of the cold energy storage device at moment; , is the charging and discharging power of the heat energy storage device respectively.

8. The power system dispatching method of claim 7, wherein, In Step 3, a carbon price model is constructed by considering the influence of the carbon price on the optimization result: ； （10） wherein: , is the carbon emission cost and the actual net emission amount; , is the base carbon price and the carbon price growth rate; is the carbon amount growth interval length; ； (11) In the formula: , is the actual carbon emission and carbon emission quota; , is the actual carbon emission coefficient and carbon emission quota per unit of electric energy; , , , is the actual carbon emission coefficient and carbon emission quota per unit of gas for gas-fired boilers and combined heat and power units; is the carbon absorption coefficient of the gas produced by the methane reactor; , is the conversion coefficient of electric heat conversion and cold heat conversion.

9. The power system dispatching method according to any one of claims 1-8, characterized in that, In Step 4, the improved sparrow search optimization algorithm is used to solve the day-ahead multi-agent collaborative optimization model to obtain the optimal scheduling scheme of the power distribution network system; Sparrows in nature are divided into explorers, followers and scouts; The explorer is a sparrow that can find better food; the follower is influenced by the direction of the explorer and follows the explorer to find the best food; the scout is always alert to the surrounding dangers and will take corresponding escape behavior when finding foragers; The population consisting of n sparrows is represented as: ；（12） In the formula, d is the dimension of the problem to be solved; n is the total number of sparrows; The position updating formula of the searcher is: ； （13） wherein: is the position of the ith explorer, ; r is the iteration number; R is the maximum iteration number; is a random number between 0 and 1; Q is a random number subject to normal distribution; L is a 1 x d order matrix, each element of which is 1; Z, respectively represent the early warning value and the safety value; The position updating formula of the follower is: ； （14） wherein: is the current optimal position; is the global worst position; A is a 1 x d matrix with elements randomly 1 or -1, and ; The position updating formula of the scout is: ； （15） wherein: is the current global optimal position; is a step control coefficient; m is a random number in the range [0, 1]; is the fitness of sparrow i; , denotes the current global worst and best fitness; is a constant to avoid division by zero.

10. The power system dispatching method of claim 9, wherein, The improved sparrow search optimization algorithm adopts an improved sparrow species dynamic adjustment strategy: The explorers in the sparrow population perform global search, and the activities of the followers are divided into two categories; part of the followers perform local search at the best position of the explorer, and the other part performs global search; the number of explorers and followers in the sparrow population is fixed in the original sparrow search algorithm, and a fixed number of explorers perform global search in each iteration; then, the followers search according to the direction provided by the explorers; once the ideal position of the follower falls into the local optimum, a certain number of followers will perform local search at the best point, and then perform global search, so it is difficult to exit the local optimum; In the later stage of the iteration process, more followers are needed to perform global search in order to explore better sites in various places; in addition, a larger proportion of sparrows need to perform global search in the followers; in order to balance the global search and local search capabilities, a dynamic adjustment strategy for the number of sparrow explorers and followers is adopted, In the formula: is the proportion of improved explorers; is the proportion of original explorers; is the upper limit of the increase of the set explorer ratio column; is the follower of sparrow in the population; is the original setting ratio; is the upper limit of the increased searcher sparrow ratio; as the number of iterations increases, the number of explorers and followers increases, and the number of followers searching around the optimal position of the explorer decreases, which helps the algorithm jump out of the local optimum and increase the global search ability.

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